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Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies. 

Circadian rhythms are nearly ubiquitous throughout nature, suggesting they are critical for survival in diverse environments. Organisms inhabiting largely arrhythmic environments, such as caves, offer a unique opportunity to study the evolution of circadian rhythms in response to changing ecological pressures. Populations of the Mexican tetra, Astyanax mexicanus , have repeatedly invaded caves from surface rivers, where individuals must contend with perpetual darkness, reduced food availability, and limited fluctuations in daily environmental cues. To investigate the molecular basis for evolved changes in circadian rhythms, we investigated rhythmic transcription across multiple independently-evolved cavefish populations. Our findings reveal that evolution in a cave environment has led to the repeated disruption of the endogenous biological clock, and its entrainment by light. The circadian transcriptome shows widespread reductions and losses of rhythmic transcription and changes to the timing of the activation/repression of core-transcriptional clock. In addition to dysregulation of the core clock, we find that rhythmic transcription of the melatonin regulator aanat2 and melatonin rhythms are disrupted in cavefish under darkness. Mutants of aanat2 and core clock gene rorca disrupt diurnal regulation of sleep in A . mexicanus , phenocopying circadian modulation of sleep and activity phenotypes of cave populations. Together, these findings reveal multiple independent mechanisms for loss of circadian rhythms in cavefish populations and provide a platform for studying how evolved changes in the biological clock can contribute to variation in sleep and circadian behavior. 

Killer toxins are extracellular antifungal proteins that are produced by a wide variety of fungi, including Saccharomyces yeasts. Although many Saccharomyces killer toxins have been previously identified, their evolutionary origins remain uncertain given that many of these genes have been mobilized by double-stranded RNA (dsRNA) viruses. A survey of yeasts from the Saccharomyces genus has identified a novel killer toxin with a unique spectrum of activity produced by Saccharomyces paradoxus . The expression of this killer toxin is associated with the presence of a dsRNA totivirus and a satellite dsRNA. Genetic sequencing of the satellite dsRNA confirmed that it encodes a killer toxin with homology to the canonical ionophoric K1 toxin from Saccharomyces cerevisiae and has been named K1-like (K1L). Genomic homologs of K1L were identified in six non- Saccharomyces yeast species of the Saccharomycotina subphylum, predominantly in subtelomeric regions of the genome. When ectopically expressed in S . cerevisiae from cloned cDNAs, both K1L and its homologs can inhibit the growth of competing yeast species, confirming the discovery of a family of biologically active K1-like killer toxins. The sporadic distribution of these genes supports their acquisition by horizontal gene transfer followed by diversification. The phylogenetic relationship between K1L and its genomic homologs suggests a common ancestry and gene flow via dsRNAs and DNAs across taxonomic divisions. This appears to enable the acquisition of a diverse arsenal of killer toxins by different yeast species for potential use in niche competition.